Overview

Dataset statistics

Number of variables39
Number of observations2930
Missing cells0
Missing cells (%)0.0%
Duplicate rows5
Duplicate rows (%)0.2%
Total size in memory892.9 KiB
Average record size in memory312.0 B

Variable types

NUM20
CAT19

Warnings

Dataset has 5 (0.2%) duplicate rows Duplicates
MasVnrArea has 1771 (60.4%) zeros Zeros
BsmtFinSF1 has 930 (31.7%) zeros Zeros
BsmtFinSF2 has 2579 (88.0%) zeros Zeros
BsmtUnfSF has 244 (8.3%) zeros Zeros
TotalBsmtSF has 79 (2.7%) zeros Zeros
2ndFlrSF has 1678 (57.3%) zeros Zeros
Fireplaces has 1422 (48.5%) zeros Zeros
GarageArea has 157 (5.4%) zeros Zeros
WoodDeckSF has 1526 (52.1%) zeros Zeros
OpenPorchSF has 1300 (44.4%) zeros Zeros

Reproduction

Analysis started2020-10-20 20:03:13.475688
Analysis finished2020-10-20 20:04:29.578872
Duration1 minute and 16.1 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

Neighborhood
Categorical

Distinct28
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size22.9 KiB
NAmes
443 
CollgCr
267 
OldTown
239 
Edwards
194 
Somerst
182 
Other values (23)
1605 
ValueCountFrequency (%) 
NAmes44315.1%
 
CollgCr2679.1%
 
OldTown2398.2%
 
Edwards1946.6%
 
Somerst1826.2%
 
NridgHt1665.7%
 
Gilbert1655.6%
 
Sawyer1515.2%
 
NWAmes1314.5%
 
SawyerW1254.3%
 
Other values (18)86729.6%
 
2020-10-20T21:04:29.762382image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1 ?
Unique (%)< 0.1%
2020-10-20T21:04:30.329124image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length7
Median length7
Mean length6.499317406
Min length5

OverallQual
Real number (ℝ≥0)

Distinct10
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.094880546
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Memory size22.9 KiB
2020-10-20T21:04:30.479985image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q15
median6
Q37
95-th percentile8
Maximum10
Range9
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.411026084
Coefficient of variation (CV)0.2315100473
Kurtosis0.05241244956
Mean6.094880546
Median Absolute Deviation (MAD)1
Skewness0.190633956
Sum17858
Variance1.990994608
MonotocityNot monotonic
2020-10-20T21:04:30.592243image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
582528.2%
 
673225.0%
 
760220.5%
 
835011.9%
 
42267.7%
 
91073.7%
 
3401.4%
 
10311.1%
 
2130.4%
 
140.1%
 
ValueCountFrequency (%) 
140.1%
 
2130.4%
 
3401.4%
 
42267.7%
 
582528.2%
 
ValueCountFrequency (%) 
10311.1%
 
91073.7%
 
835011.9%
 
760220.5%
 
673225.0%
 

OverallCond
Real number (ℝ≥0)

Distinct9
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.563139932
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Memory size22.9 KiB
2020-10-20T21:04:30.716744image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q15
median5
Q36
95-th percentile8
Maximum9
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.11153656
Coefficient of variation (CV)0.1998038111
Kurtosis1.491449722
Mean5.563139932
Median Absolute Deviation (MAD)0
Skewness0.574429477
Sum16300
Variance1.235513524
MonotocityNot monotonic
2020-10-20T21:04:30.832081image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%) 
5165456.5%
 
653318.2%
 
739013.3%
 
81444.9%
 
41013.4%
 
3501.7%
 
9411.4%
 
2100.3%
 
170.2%
 
ValueCountFrequency (%) 
170.2%
 
2100.3%
 
3501.7%
 
41013.4%
 
5165456.5%
 
ValueCountFrequency (%) 
9411.4%
 
81444.9%
 
739013.3%
 
653318.2%
 
5165456.5%
 

YearBuilt
Real number (ℝ≥0)

Distinct118
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1971.356314
Minimum1872
Maximum2010
Zeros0
Zeros (%)0.0%
Memory size22.9 KiB
2020-10-20T21:04:30.984291image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1872
5-th percentile1915
Q11954
median1973
Q32001
95-th percentile2007
Maximum2010
Range138
Interquartile range (IQR)47

Descriptive statistics

Standard deviation30.24536063
Coefficient of variation (CV)0.01534241193
Kurtosis-0.5017150401
Mean1971.356314
Median Absolute Deviation (MAD)25
Skewness-0.6044622214
Sum5776074
Variance914.7818396
MonotocityNot monotonic
2020-10-20T21:04:31.180396image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
20051424.8%
 
20061384.7%
 
20071093.7%
 
2004993.4%
 
2003883.0%
 
1977571.9%
 
1920571.9%
 
1976541.8%
 
1999521.8%
 
2008491.7%
 
Other values (108)208571.2%
 
ValueCountFrequency (%) 
18721< 0.1%
 
18751< 0.1%
 
18791< 0.1%
 
188050.2%
 
18821< 0.1%
 
ValueCountFrequency (%) 
201030.1%
 
2009250.9%
 
2008491.7%
 
20071093.7%
 
20061384.7%
 

RoofStyle
Categorical

Distinct6
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size22.9 KiB
Gable
2321 
Hip
551 
Gambrel
 
22
Flat
 
20
Mansard
 
11
ValueCountFrequency (%) 
Gable232179.2%
 
Hip55118.8%
 
Gambrel220.8%
 
Flat200.7%
 
Mansard110.4%
 
Shed50.2%
 
2020-10-20T21:04:31.386205image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-20T21:04:31.492027image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T21:04:31.628729image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length7
Median length5
Mean length4.637883959
Min length3

Exterior1st
Categorical

Distinct16
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size22.9 KiB
VinylSd
1026 
MetalSd
450 
HdBoard
442 
Wd Sdng
420 
Plywood
221 
Other values (11)
371 
ValueCountFrequency (%) 
VinylSd102635.0%
 
MetalSd45015.4%
 
HdBoard44215.1%
 
Wd Sdng42014.3%
 
Plywood2217.5%
 
CemntBd1264.3%
 
BrkFace883.0%
 
WdShing561.9%
 
AsbShng441.5%
 
Stucco431.5%
 
Other values (6)140.5%
 
2020-10-20T21:04:31.778770image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique2 ?
Unique (%)0.1%
2020-10-20T21:04:31.925351image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length7
Median length7
Mean length6.983276451
Min length5

Exterior2nd
Categorical

Distinct17
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size22.9 KiB
VinylSd
1015 
MetalSd
447 
HdBoard
406 
Wd Sdng
397 
Plywood
274 
Other values (12)
391 
ValueCountFrequency (%) 
VinylSd101534.6%
 
MetalSd44715.3%
 
HdBoard40613.9%
 
Wd Sdng39713.5%
 
Plywood2749.4%
 
CmentBd1264.3%
 
Wd Shng812.8%
 
BrkFace471.6%
 
Stucco471.6%
 
AsbShng381.3%
 
Other values (7)521.8%
 
2020-10-20T21:04:32.081113image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique2 ?
Unique (%)0.1%
2020-10-20T21:04:32.224454image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length7
Median length7
Mean length6.978156997
Min length5

MasVnrType
Categorical

Distinct5
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size22.9 KiB
None
1775 
BrkFace
880 
Stone
249 
BrkCmn
 
25
CBlock
 
1
ValueCountFrequency (%) 
None177560.6%
 
BrkFace88030.0%
 
Stone2498.5%
 
BrkCmn250.9%
 
CBlock1< 0.1%
 
2020-10-20T21:04:32.388766image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1 ?
Unique (%)< 0.1%
2020-10-20T21:04:32.496795image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T21:04:32.618337image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length7
Median length4
Mean length5.003754266
Min length4

MasVnrArea
Real number (ℝ≥0)

ZEROS

Distinct445
Distinct (%)15.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean101.0969283
Minimum0
Maximum1600
Zeros1771
Zeros (%)60.4%
Memory size22.9 KiB
2020-10-20T21:04:32.773971image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3162.75
95-th percentile466
Maximum1600
Range1600
Interquartile range (IQR)162.75

Descriptive statistics

Standard deviation178.6345448
Coefficient of variation (CV)1.766963129
Kurtosis9.368695949
Mean101.0969283
Median Absolute Deviation (MAD)0
Skewness2.61930513
Sum296214
Variance31910.30061
MonotocityNot monotonic
2020-10-20T21:04:32.949387image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0177160.4%
 
120150.5%
 
176130.4%
 
200130.4%
 
216120.4%
 
180120.4%
 
144110.4%
 
108110.4%
 
72110.4%
 
16110.4%
 
Other values (435)105035.8%
 
ValueCountFrequency (%) 
0177160.4%
 
130.1%
 
31< 0.1%
 
111< 0.1%
 
1440.1%
 
ValueCountFrequency (%) 
16001< 0.1%
 
13781< 0.1%
 
12901< 0.1%
 
122420.1%
 
11701< 0.1%
 

ExterQual
Categorical

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size22.9 KiB
TA
1799 
Gd
989 
Ex
 
107
Fa
 
35
ValueCountFrequency (%) 
TA179961.4%
 
Gd98933.8%
 
Ex1073.7%
 
Fa351.2%
 
2020-10-20T21:04:33.144426image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-20T21:04:33.237662image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T21:04:33.357915image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length2
Mean length2
Min length2

Foundation
Categorical

Distinct6
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size22.9 KiB
PConc
1310 
CBlock
1244 
BrkTil
311 
Slab
 
49
Stone
 
11
ValueCountFrequency (%) 
PConc131044.7%
 
CBlock124442.5%
 
BrkTil31110.6%
 
Slab491.7%
 
Stone110.4%
 
Wood50.2%
 
2020-10-20T21:04:33.486941image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-20T21:04:33.582826image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T21:04:33.723071image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length6
Median length6
Mean length5.512286689
Min length4

BsmtQual
Categorical

Distinct5
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size22.9 KiB
TA
1363 
Gd
1219 
Ex
258 
Fa
 
88
Po
 
2
ValueCountFrequency (%) 
TA136346.5%
 
Gd121941.6%
 
Ex2588.8%
 
Fa883.0%
 
Po20.1%
 
2020-10-20T21:04:33.885534image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-20T21:04:33.994930image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T21:04:34.133321image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length2
Mean length2
Min length2

BsmtExposure
Categorical

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size22.9 KiB
No
1989 
Av
418 
Gd
284 
Mn
239 
ValueCountFrequency (%) 
No198967.9%
 
Av41814.3%
 
Gd2849.7%
 
Mn2398.2%
 
2020-10-20T21:04:34.275956image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-20T21:04:34.380939image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T21:04:34.489853image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length2
Mean length2
Min length2

BsmtFinType1
Categorical

Distinct6
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size22.9 KiB
GLQ
939 
Unf
851 
ALQ
429 
Rec
288 
BLQ
269 
ValueCountFrequency (%) 
GLQ93932.0%
 
Unf85129.0%
 
ALQ42914.6%
 
Rec2889.8%
 
BLQ2699.2%
 
LwQ1545.3%
 
2020-10-20T21:04:34.619994image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-20T21:04:34.731325image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T21:04:34.861703image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length3
Mean length3
Min length3

BsmtFinSF1
Real number (ℝ≥0)

ZEROS

Distinct995
Distinct (%)34.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean442.6047782
Minimum0
Maximum5644
Zeros930
Zeros (%)31.7%
Memory size22.9 KiB
2020-10-20T21:04:35.009632image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median370
Q3734
95-th percentile1274
Maximum5644
Range5644
Interquartile range (IQR)734

Descriptive statistics

Standard deviation455.5150362
Coefficient of variation (CV)1.029168818
Kurtosis6.862784985
Mean442.6047782
Median Absolute Deviation (MAD)370
Skewness1.416567236
Sum1296832
Variance207493.9482
MonotocityNot monotonic
2020-10-20T21:04:35.173107image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
093031.7%
 
24270.9%
 
16140.5%
 
30090.3%
 
28880.3%
 
38480.3%
 
2080.3%
 
60080.3%
 
45670.2%
 
36070.2%
 
Other values (985)190465.0%
 
ValueCountFrequency (%) 
093031.7%
 
21< 0.1%
 
16140.5%
 
2080.3%
 
24270.9%
 
ValueCountFrequency (%) 
56441< 0.1%
 
40101< 0.1%
 
22881< 0.1%
 
22601< 0.1%
 
22571< 0.1%
 

BsmtFinType2
Categorical

Distinct6
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size22.9 KiB
Unf
2580 
Rec
 
106
LwQ
 
89
BLQ
 
68
ALQ
 
53
ValueCountFrequency (%) 
Unf258088.1%
 
Rec1063.6%
 
LwQ893.0%
 
BLQ682.3%
 
ALQ531.8%
 
GLQ341.2%
 
2020-10-20T21:04:35.337004image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-20T21:04:35.428726image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T21:04:35.562465image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length3
Mean length3
Min length3

BsmtFinSF2
Real number (ℝ≥0)

ZEROS

Distinct274
Distinct (%)9.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.70546075
Minimum0
Maximum1526
Zeros2579
Zeros (%)88.0%
Memory size22.9 KiB
2020-10-20T21:04:35.712418image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile435
Maximum1526
Range1526
Interquartile range (IQR)0

Descriptive statistics

Standard deviation169.1420893
Coefficient of variation (CV)3.402887464
Kurtosis18.78928835
Mean49.70546075
Median Absolute Deviation (MAD)0
Skewness4.140793743
Sum145637
Variance28609.04637
MonotocityNot monotonic
2020-10-20T21:04:35.869076image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0257988.0%
 
29450.2%
 
18050.2%
 
7230.1%
 
43530.1%
 
48330.1%
 
14730.1%
 
37430.1%
 
16230.1%
 
18230.1%
 
Other values (264)32010.9%
 
ValueCountFrequency (%) 
0257988.0%
 
61< 0.1%
 
121< 0.1%
 
281< 0.1%
 
321< 0.1%
 
ValueCountFrequency (%) 
15261< 0.1%
 
14741< 0.1%
 
13931< 0.1%
 
11641< 0.1%
 
11271< 0.1%
 

BsmtUnfSF
Real number (ℝ≥0)

ZEROS

Distinct1137
Distinct (%)38.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean559.2307167
Minimum0
Maximum2336
Zeros244
Zeros (%)8.3%
Memory size22.9 KiB
2020-10-20T21:04:36.060038image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1219
median466
Q3801.75
95-th percentile1473.55
Maximum2336
Range2336
Interquartile range (IQR)582.75

Descriptive statistics

Standard deviation439.4224996
Coefficient of variation (CV)0.7857624527
Kurtosis0.4108531189
Mean559.2307167
Median Absolute Deviation (MAD)280
Skewness0.923403041
Sum1638546
Variance193092.1332
MonotocityNot monotonic
2020-10-20T21:04:36.218122image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
02448.3%
 
384190.6%
 
728140.5%
 
672130.4%
 
600120.4%
 
216110.4%
 
816110.4%
 
572110.4%
 
100110.4%
 
270100.3%
 
Other values (1127)257487.8%
 
ValueCountFrequency (%) 
02448.3%
 
141< 0.1%
 
151< 0.1%
 
171< 0.1%
 
201< 0.1%
 
ValueCountFrequency (%) 
23361< 0.1%
 
21531< 0.1%
 
21401< 0.1%
 
21211< 0.1%
 
20621< 0.1%
 

TotalBsmtSF
Real number (ℝ≥0)

ZEROS

Distinct1058
Distinct (%)36.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1051.593515
Minimum0
Maximum6110
Zeros79
Zeros (%)2.7%
Memory size22.9 KiB
2020-10-20T21:04:36.381849image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile453.25
Q1793
median990
Q31301.5
95-th percentile1776
Maximum6110
Range6110
Interquartile range (IQR)508.5

Descriptive statistics

Standard deviation440.5413151
Coefficient of variation (CV)0.418927379
Kurtosis9.139809725
Mean1051.593515
Median Absolute Deviation (MAD)236
Skewness1.156532259
Sum3081169
Variance194076.6504
MonotocityNot monotonic
2020-10-20T21:04:36.538624image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0792.7%
 
864742.5%
 
672291.0%
 
912260.9%
 
1040250.9%
 
768240.8%
 
816230.8%
 
728210.7%
 
780190.6%
 
1008190.6%
 
Other values (1048)259188.4%
 
ValueCountFrequency (%) 
0792.7%
 
1051< 0.1%
 
1601< 0.1%
 
1731< 0.1%
 
1901< 0.1%
 
ValueCountFrequency (%) 
61101< 0.1%
 
50951< 0.1%
 
32061< 0.1%
 
32001< 0.1%
 
31381< 0.1%
 

Heating
Categorical

Distinct6
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size22.9 KiB
GasA
2885 
GasW
 
27
Grav
 
9
Wall
 
6
OthW
 
2
ValueCountFrequency (%) 
GasA288598.5%
 
GasW270.9%
 
Grav90.3%
 
Wall60.2%
 
OthW20.1%
 
Floor1< 0.1%
 
2020-10-20T21:04:36.691820image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1 ?
Unique (%)< 0.1%
2020-10-20T21:04:36.795660image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T21:04:36.930076image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length5
Median length4
Mean length4.000341297
Min length4

HeatingQC
Categorical

Distinct5
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size22.9 KiB
Ex
1495 
TA
864 
Gd
476 
Fa
 
92
Po
 
3
ValueCountFrequency (%) 
Ex149551.0%
 
TA86429.5%
 
Gd47616.2%
 
Fa923.1%
 
Po30.1%
 
2020-10-20T21:04:37.073343image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-20T21:04:37.192225image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T21:04:37.299550image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length2
Mean length2
Min length2

1stFlrSF
Real number (ℝ≥0)

Distinct1083
Distinct (%)37.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1159.557679
Minimum334
Maximum5095
Zeros0
Zeros (%)0.0%
Memory size22.9 KiB
2020-10-20T21:04:37.429450image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum334
5-th percentile665.45
Q1876.25
median1084
Q31384
95-th percentile1829.55
Maximum5095
Range4761
Interquartile range (IQR)507.75

Descriptive statistics

Standard deviation391.8908853
Coefficient of variation (CV)0.3379658402
Kurtosis6.968808529
Mean1159.557679
Median Absolute Deviation (MAD)236
Skewness1.46942864
Sum3397504
Variance153578.4659
MonotocityNot monotonic
2020-10-20T21:04:37.594159image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
864461.6%
 
1040281.0%
 
912190.6%
 
816180.6%
 
848180.6%
 
960180.6%
 
894170.6%
 
936170.6%
 
672170.6%
 
546150.5%
 
Other values (1073)271792.7%
 
ValueCountFrequency (%) 
3341< 0.1%
 
3721< 0.1%
 
4071< 0.1%
 
4321< 0.1%
 
4381< 0.1%
 
ValueCountFrequency (%) 
50951< 0.1%
 
46921< 0.1%
 
38201< 0.1%
 
32281< 0.1%
 
31381< 0.1%
 

2ndFlrSF
Real number (ℝ≥0)

ZEROS

Distinct635
Distinct (%)21.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean335.4559727
Minimum0
Maximum2065
Zeros1678
Zeros (%)57.3%
Memory size22.9 KiB
2020-10-20T21:04:37.775971image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3703.75
95-th percentile1130.1
Maximum2065
Range2065
Interquartile range (IQR)703.75

Descriptive statistics

Standard deviation428.395715
Coefficient of variation (CV)1.277054964
Kurtosis-0.4148612968
Mean335.4559727
Median Absolute Deviation (MAD)0
Skewness0.8664567508
Sum982886
Variance183522.8886
MonotocityNot monotonic
2020-10-20T21:04:37.932730image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0167857.3%
 
546230.8%
 
728180.6%
 
504170.6%
 
672130.4%
 
600130.4%
 
720130.4%
 
896110.4%
 
886100.3%
 
78090.3%
 
Other values (625)112538.4%
 
ValueCountFrequency (%) 
0167857.3%
 
1101< 0.1%
 
1251< 0.1%
 
1441< 0.1%
 
1671< 0.1%
 
ValueCountFrequency (%) 
20651< 0.1%
 
18721< 0.1%
 
18621< 0.1%
 
18361< 0.1%
 
18181< 0.1%
 

GrLivArea
Real number (ℝ≥0)

Distinct1292
Distinct (%)44.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1499.690444
Minimum334
Maximum5642
Zeros0
Zeros (%)0.0%
Memory size22.9 KiB
2020-10-20T21:04:38.083835image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum334
5-th percentile861
Q11126
median1442
Q31742.75
95-th percentile2463.1
Maximum5642
Range5308
Interquartile range (IQR)616.75

Descriptive statistics

Standard deviation505.5088875
Coefficient of variation (CV)0.3370754875
Kurtosis4.137838193
Mean1499.690444
Median Absolute Deviation (MAD)311
Skewness1.274109716
Sum4394093
Variance255539.2353
MonotocityNot monotonic
2020-10-20T21:04:38.237644image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
864411.4%
 
1092260.9%
 
1040250.9%
 
1456200.7%
 
1200180.6%
 
894150.5%
 
912140.5%
 
816140.5%
 
848130.4%
 
1728130.4%
 
Other values (1282)273193.2%
 
ValueCountFrequency (%) 
3341< 0.1%
 
4071< 0.1%
 
4381< 0.1%
 
4801< 0.1%
 
4921< 0.1%
 
ValueCountFrequency (%) 
56421< 0.1%
 
50951< 0.1%
 
46761< 0.1%
 
44761< 0.1%
 
43161< 0.1%
 

BsmtFullBath
Categorical

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size22.9 KiB
0
1709 
1
1181 
2
 
38
3
 
2
ValueCountFrequency (%) 
0170958.3%
 
1118140.3%
 
2381.3%
 
320.1%
 
2020-10-20T21:04:38.385865image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-20T21:04:38.477706image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T21:04:38.578505image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length3
Mean length3
Min length3

FullBath
Real number (ℝ≥0)

Distinct5
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.566552901
Minimum0
Maximum4
Zeros12
Zeros (%)0.4%
Memory size22.9 KiB
2020-10-20T21:04:38.683199image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median2
Q32
95-th percentile2
Maximum4
Range4
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.5529406116
Coefficient of variation (CV)0.3529664471
Kurtosis-0.5414370373
Mean1.566552901
Median Absolute Deviation (MAD)0
Skewness0.1719520775
Sum4590
Variance0.30574332
MonotocityNot monotonic
2020-10-20T21:04:38.794996image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=5)
ValueCountFrequency (%) 
2153252.3%
 
1131845.0%
 
3642.2%
 
0120.4%
 
440.1%
 
ValueCountFrequency (%) 
0120.4%
 
1131845.0%
 
2153252.3%
 
3642.2%
 
440.1%
 
ValueCountFrequency (%) 
440.1%
 
3642.2%
 
2153252.3%
 
1131845.0%
 
0120.4%
 

HalfBath
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size22.9 KiB
0
1843 
1
1062 
2
 
25
ValueCountFrequency (%) 
0184362.9%
 
1106236.2%
 
2250.9%
 
2020-10-20T21:04:38.932902image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-20T21:04:39.020422image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T21:04:39.111734image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length1
Median length1
Mean length1
Min length1

BedroomAbvGr
Real number (ℝ≥0)

Distinct8
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.854266212
Minimum0
Maximum8
Zeros8
Zeros (%)0.3%
Memory size22.9 KiB
2020-10-20T21:04:39.216800image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q12
median3
Q33
95-th percentile4
Maximum8
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.827731142
Coefficient of variation (CV)0.2899978771
Kurtosis1.891420659
Mean2.854266212
Median Absolute Deviation (MAD)0
Skewness0.3056942114
Sum8363
Variance0.6851388434
MonotocityNot monotonic
2020-10-20T21:04:39.333032image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%) 
3159754.5%
 
274325.4%
 
440013.7%
 
11123.8%
 
5481.6%
 
6210.7%
 
080.3%
 
81< 0.1%
 
ValueCountFrequency (%) 
080.3%
 
11123.8%
 
274325.4%
 
3159754.5%
 
440013.7%
 
ValueCountFrequency (%) 
81< 0.1%
 
6210.7%
 
5481.6%
 
440013.7%
 
3159754.5%
 

KitchenQual
Categorical

Distinct5
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size22.9 KiB
TA
1494 
Gd
1160 
Ex
205 
Fa
 
70
Po
 
1
ValueCountFrequency (%) 
TA149451.0%
 
Gd116039.6%
 
Ex2057.0%
 
Fa702.4%
 
Po1< 0.1%
 
2020-10-20T21:04:39.480610image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1 ?
Unique (%)< 0.1%
2020-10-20T21:04:39.572855image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T21:04:39.682918image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length2
Mean length2
Min length2

TotRmsAbvGrd
Real number (ℝ≥0)

Distinct14
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.443003413
Minimum2
Maximum15
Zeros0
Zeros (%)0.0%
Memory size22.9 KiB
2020-10-20T21:04:39.797429image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile4
Q15
median6
Q37
95-th percentile9
Maximum15
Range13
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.572964396
Coefficient of variation (CV)0.2441352729
Kurtosis1.154588179
Mean6.443003413
Median Absolute Deviation (MAD)1
Skewness0.7535425619
Sum18878
Variance2.474216992
MonotocityNot monotonic
2020-10-20T21:04:39.923226image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%) 
684428.8%
 
764922.2%
 
558620.0%
 
834711.8%
 
42036.9%
 
91434.9%
 
10802.7%
 
11321.1%
 
3260.9%
 
12160.5%
 
Other values (4)40.1%
 
ValueCountFrequency (%) 
21< 0.1%
 
3260.9%
 
42036.9%
 
558620.0%
 
684428.8%
 
ValueCountFrequency (%) 
151< 0.1%
 
141< 0.1%
 
131< 0.1%
 
12160.5%
 
11321.1%
 

Fireplaces
Real number (ℝ≥0)

ZEROS

Distinct5
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5993174061
Minimum0
Maximum4
Zeros1422
Zeros (%)48.5%
Memory size22.9 KiB
2020-10-20T21:04:40.038816image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile2
Maximum4
Range4
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.6479209166
Coefficient of variation (CV)1.081098112
Kurtosis0.1015084776
Mean0.5993174061
Median Absolute Deviation (MAD)1
Skewness0.7392152012
Sum1756
Variance0.4198015141
MonotocityNot monotonic
2020-10-20T21:04:40.146567image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=5)
ValueCountFrequency (%) 
0142248.5%
 
1127443.5%
 
22217.5%
 
3120.4%
 
41< 0.1%
 
ValueCountFrequency (%) 
0142248.5%
 
1127443.5%
 
22217.5%
 
3120.4%
 
41< 0.1%
 
ValueCountFrequency (%) 
41< 0.1%
 
3120.4%
 
22217.5%
 
1127443.5%
 
0142248.5%
 

GarageType
Categorical

Distinct6
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size22.9 KiB
Attchd
1888 
Detchd
782 
BuiltIn
 
186
Basment
 
36
2Types
 
23
ValueCountFrequency (%) 
Attchd188864.4%
 
Detchd78226.7%
 
BuiltIn1866.3%
 
Basment361.2%
 
2Types230.8%
 
CarPort150.5%
 
2020-10-20T21:04:40.316578image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-20T21:04:40.451138image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T21:04:40.569204image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length7
Median length6
Mean length6.080887372
Min length6

GarageYrBlt
Real number (ℝ≥0)

Distinct103
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1978.179522
Minimum1895
Maximum2207
Zeros0
Zeros (%)0.0%
Memory size22.9 KiB
2020-10-20T21:04:40.700853image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1895
5-th percentile1930
Q11962
median1979
Q32001
95-th percentile2007
Maximum2207
Range312
Interquartile range (IQR)39

Descriptive statistics

Standard deviation24.82662017
Coefficient of variation (CV)0.01255023616
Kurtosis2.105775684
Mean1978.179522
Median Absolute Deviation (MAD)20
Skewness-0.4011957539
Sum5796066
Variance616.3610691
MonotocityNot monotonic
2020-10-20T21:04:40.854330image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
19791946.6%
 
20051424.8%
 
20071153.9%
 
20061153.9%
 
2004993.4%
 
2003923.1%
 
1977662.3%
 
2008612.1%
 
1998592.0%
 
2000551.9%
 
Other values (93)193265.9%
 
ValueCountFrequency (%) 
18951< 0.1%
 
18961< 0.1%
 
190060.2%
 
19061< 0.1%
 
19081< 0.1%
 
ValueCountFrequency (%) 
22071< 0.1%
 
201050.2%
 
2009291.0%
 
2008612.1%
 
20071153.9%
 

GarageFinish
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size22.9 KiB
Unf
1390 
RFn
812 
Fin
728 
ValueCountFrequency (%) 
Unf139047.4%
 
RFn81227.7%
 
Fin72824.8%
 
2020-10-20T21:04:41.012432image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-20T21:04:41.102522image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T21:04:41.191326image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length3
Mean length3
Min length3

GarageArea
Real number (ℝ≥0)

ZEROS

Distinct603
Distinct (%)20.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean472.8221843
Minimum0
Maximum1488
Zeros157
Zeros (%)5.4%
Memory size22.9 KiB
2020-10-20T21:04:41.326257image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1320
median480
Q3576
95-th percentile856
Maximum1488
Range1488
Interquartile range (IQR)256

Descriptive statistics

Standard deviation215.0098764
Coefficient of variation (CV)0.4547372851
Kurtosis0.9523576576
Mean472.8221843
Median Absolute Deviation (MAD)123
Skewness0.242001162
Sum1385369
Variance46229.24696
MonotocityNot monotonic
2020-10-20T21:04:41.520798image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
01575.4%
 
576973.3%
 
440963.3%
 
484762.6%
 
240692.4%
 
528652.2%
 
400582.0%
 
480551.9%
 
264511.7%
 
288501.7%
 
Other values (593)215673.6%
 
ValueCountFrequency (%) 
01575.4%
 
1001< 0.1%
 
16030.1%
 
16220.1%
 
16420.1%
 
ValueCountFrequency (%) 
14881< 0.1%
 
14181< 0.1%
 
13901< 0.1%
 
13561< 0.1%
 
13481< 0.1%
 

WoodDeckSF
Real number (ℝ≥0)

ZEROS

Distinct380
Distinct (%)13.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean93.75187713
Minimum0
Maximum1424
Zeros1526
Zeros (%)52.1%
Memory size22.9 KiB
2020-10-20T21:04:41.711628image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3168
95-th percentile327.55
Maximum1424
Range1424
Interquartile range (IQR)168

Descriptive statistics

Standard deviation126.3615619
Coefficient of variation (CV)1.347829673
Kurtosis6.753955238
Mean93.75187713
Median Absolute Deviation (MAD)0
Skewness1.842678099
Sum274693
Variance15967.24432
MonotocityNot monotonic
2020-10-20T21:04:41.900136image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0152652.1%
 
100742.5%
 
192702.4%
 
144612.1%
 
168561.9%
 
120531.8%
 
140291.0%
 
240200.7%
 
224190.6%
 
160170.6%
 
Other values (370)100534.3%
 
ValueCountFrequency (%) 
0152652.1%
 
41< 0.1%
 
1220.1%
 
141< 0.1%
 
161< 0.1%
 
ValueCountFrequency (%) 
14241< 0.1%
 
8701< 0.1%
 
8571< 0.1%
 
7361< 0.1%
 
7281< 0.1%
 

OpenPorchSF
Real number (ℝ≥0)

ZEROS

Distinct252
Distinct (%)8.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.5334471
Minimum0
Maximum742
Zeros1300
Zeros (%)44.4%
Memory size22.9 KiB
2020-10-20T21:04:42.559116image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median27
Q370
95-th percentile182.55
Maximum742
Range742
Interquartile range (IQR)70

Descriptive statistics

Standard deviation67.48340014
Coefficient of variation (CV)1.419703477
Kurtosis10.9543433
Mean47.5334471
Median Absolute Deviation (MAD)27
Skewness2.535385919
Sum139273
Variance4554.009294
MonotocityNot monotonic
2020-10-20T21:04:42.715307image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0130044.4%
 
48521.8%
 
36521.8%
 
40441.5%
 
32381.3%
 
24361.2%
 
28351.2%
 
20331.1%
 
30311.1%
 
60301.0%
 
Other values (242)127943.7%
 
ValueCountFrequency (%) 
0130044.4%
 
41< 0.1%
 
61< 0.1%
 
81< 0.1%
 
1020.1%
 
ValueCountFrequency (%) 
7421< 0.1%
 
5701< 0.1%
 
5471< 0.1%
 
5231< 0.1%
 
5021< 0.1%
 

SaleCondition
Categorical

Distinct6
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size22.9 KiB
Normal
2413 
Partial
245 
Abnorml
 
190
Family
 
46
Alloca
 
24
ValueCountFrequency (%) 
Normal241382.4%
 
Partial2458.4%
 
Abnorml1906.5%
 
Family461.6%
 
Alloca240.8%
 
AdjLand120.4%
 
2020-10-20T21:04:42.866103image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-20T21:04:42.946934image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T21:04:43.063636image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length7
Median length6
Mean length6.152559727
Min length6

SalePrice
Real number (ℝ≥0)

Distinct1032
Distinct (%)35.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean180796.0601
Minimum12789
Maximum755000
Zeros0
Zeros (%)0.0%
Memory size22.9 KiB
2020-10-20T21:04:43.195180image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum12789
5-th percentile87500
Q1129500
median160000
Q3213500
95-th percentile335000
Maximum755000
Range742211
Interquartile range (IQR)84000

Descriptive statistics

Standard deviation79886.69236
Coefficient of variation (CV)0.4418608034
Kurtosis5.118899951
Mean180796.0601
Median Absolute Deviation (MAD)37000
Skewness1.743500076
Sum529732456
Variance6381883616
MonotocityNot monotonic
2020-10-20T21:04:43.350035image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
135000341.2%
 
140000331.1%
 
130000291.0%
 
155000281.0%
 
145000260.9%
 
160000230.8%
 
110000210.7%
 
185000210.7%
 
115000200.7%
 
170000200.7%
 
Other values (1022)267591.3%
 
ValueCountFrequency (%) 
127891< 0.1%
 
131001< 0.1%
 
349001< 0.1%
 
350001< 0.1%
 
353111< 0.1%
 
ValueCountFrequency (%) 
7550001< 0.1%
 
7450001< 0.1%
 
6250001< 0.1%
 
6150001< 0.1%
 
6116571< 0.1%
 

Interactions

2020-10-20T21:03:23.916581image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T21:03:24.083709image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T21:03:24.213798image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T21:03:24.340351image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T21:03:24.485708image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T21:03:24.625850image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T21:03:24.776021image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T21:03:25.369212image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T21:03:25.517904image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T21:03:25.662639image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T21:03:25.797443image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T21:03:25.930032image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T21:03:26.085838image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T21:03:26.237259image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T21:03:26.366350image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T21:03:26.516717image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T21:03:26.655632image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T21:03:26.792139image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T21:03:26.927581image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T21:03:27.078901image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T21:03:27.229371image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T21:03:27.377199image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T21:03:27.537517image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T21:03:27.694077image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T21:03:27.855880image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T21:03:28.010594image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T21:03:28.182934image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T21:03:28.346598image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T21:03:28.509139image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T21:03:28.673714image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T21:03:28.832745image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T21:03:28.988671image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T21:03:29.152625image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T21:03:29.309933image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T21:03:29.479050image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T21:03:29.685760image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T21:03:29.956551image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T21:03:30.127822image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T21:03:30.289425image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T21:03:30.450273image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T21:03:30.619669image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T21:03:30.771108image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T21:03:30.930002image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T21:03:31.080759image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T21:03:31.232513image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T21:03:31.370621image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T21:03:31.533626image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T21:03:31.670775image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T21:03:31.817543image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T21:03:31.957604image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T21:03:32.109749image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T21:03:32.253440image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T21:03:32.411464image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T21:03:32.580005image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T21:03:32.727116image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T21:03:32.888137image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T21:03:33.039678image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T21:03:33.187214image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T21:03:33.345241image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T21:03:33.507018image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T21:03:33.666284image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T21:03:33.813064image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T21:03:33.966016image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T21:03:34.130152image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T21:03:34.298821image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T21:03:34.446847image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T21:03:34.621304image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T21:03:34.771206image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T21:03:34.935718image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T21:03:35.089316image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T21:03:35.246559image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T21:03:35.538424image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T21:03:35.695379image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T21:03:35.861004image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T21:03:36.030591image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T21:03:36.195317image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2020-10-20T21:03:36.513616image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T21:03:36.665153image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2020-10-20T21:03:36.980651image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2020-10-20T21:03:37.242610image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T21:03:37.381700image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T21:03:37.521261image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T21:03:37.667578image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2020-10-20T21:03:38.678973image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T21:03:38.848191image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T21:03:39.025444image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2020-10-20T21:03:39.486754image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2020-10-20T21:03:42.679267image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2020-10-20T21:03:43.153438image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T21:03:43.327160image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T21:03:43.488541image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T21:03:43.620090image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T21:03:43.771909image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2020-10-20T21:03:44.347345image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2020-10-20T21:03:44.626561image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T21:03:44.757406image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2020-10-20T21:03:45.167684image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T21:03:45.315485image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T21:03:45.450663image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2020-10-20T21:03:46.921759image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2020-10-20T21:03:48.275820image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2020-10-20T21:03:48.834062image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T21:03:48.994155image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2020-10-20T21:03:49.301339image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T21:03:49.458865image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T21:03:49.589430image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T21:03:49.727657image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T21:03:49.869078image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T21:03:50.020932image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2020-10-20T21:03:50.333670image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T21:03:50.467745image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T21:03:50.798021image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T21:03:50.919666image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T21:03:51.058084image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T21:03:51.190499image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T21:03:51.346460image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T21:03:51.489994image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T21:03:51.647234image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T21:03:51.800670image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T21:03:51.936262image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T21:03:52.072813image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T21:03:52.213297image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2020-10-20T21:04:24.891008image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T21:04:25.056048image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T21:04:25.201068image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T21:04:25.368831image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T21:04:25.521165image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T21:04:25.665122image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T21:04:25.813856image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T21:04:25.985297image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T21:04:26.136042image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T21:04:26.301672image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T21:04:26.476169image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T21:04:26.632372image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T21:04:26.806112image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T21:04:26.970355image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T21:04:27.128338image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2020-10-20T21:04:43.533233image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-10-20T21:04:43.865980image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-10-20T21:04:44.188057image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-10-20T21:04:44.548260image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2020-10-20T21:04:44.970662image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2020-10-20T21:04:27.686524image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-20T21:04:29.236674image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Sample

First rows

NeighborhoodOverallQualOverallCondYearBuiltRoofStyleExterior1stExterior2ndMasVnrTypeMasVnrAreaExterQualFoundationBsmtQualBsmtExposureBsmtFinType1BsmtFinSF1BsmtFinType2BsmtFinSF2BsmtUnfSFTotalBsmtSFHeatingHeatingQC1stFlrSF2ndFlrSFGrLivAreaBsmtFullBathFullBathHalfBathBedroomAbvGrKitchenQualTotRmsAbvGrdFireplacesGarageTypeGarageYrBltGarageFinishGarageAreaWoodDeckSFOpenPorchSFSaleConditionSalePrice
0NAmes651960HipBrkFacePlywoodStone112.0TACBlockTAGdBLQ639.0Unf0.0441.01080.0GasAFa1656016561.0103TA72Attchd1960.0Fin528.021062Normal215000
1NAmes561961GableVinylSdVinylSdNone0.0TACBlockTANoRec468.0LwQ144.0270.0882.0GasATA89608960.0102TA50Attchd1961.0Unf730.01400Normal105000
2NAmes661958HipWd SdngWd SdngBrkFace108.0TACBlockTANoALQ923.0Unf0.0406.01329.0GasATA1329013290.0113Gd60Attchd1958.0Unf312.039336Normal172000
3NAmes751968HipBrkFaceBrkFaceNone0.0GdCBlockTANoALQ1065.0Unf0.01045.02110.0GasAEx2110021101.0213Ex82Attchd1968.0Fin522.000Normal244000
4Gilbert551997GableVinylSdVinylSdNone0.0TAPConcGdNoGLQ791.0Unf0.0137.0928.0GasAGd92870116290.0213TA61Attchd1997.0Fin482.021234Normal189900
5Gilbert661998GableVinylSdVinylSdBrkFace20.0TAPConcTANoGLQ602.0Unf0.0324.0926.0GasAEx92667816040.0213Gd71Attchd1998.0Fin470.036036Normal195500
6StoneBr852001GableCemntBdCmentBdNone0.0GdPConcGdMnGLQ616.0Unf0.0722.01338.0GasAEx1338013381.0202Gd60Attchd2001.0Fin582.000Normal213500
7StoneBr851992GableHdBoardHdBoardNone0.0GdPConcGdNoALQ263.0Unf0.01017.01280.0GasAEx1280012800.0202Gd50Attchd1992.0RFn506.0082Normal191500
8StoneBr851995GableCemntBdCmentBdNone0.0GdPConcGdNoGLQ1180.0Unf0.0415.01595.0GasAEx1616016161.0202Gd51Attchd1995.0RFn608.0237152Normal236500
9Gilbert751999GableVinylSdVinylSdNone0.0TAPConcTANoUnf0.0Unf0.0994.0994.0GasAGd102877618040.0213Gd71Attchd1999.0Fin442.014060Normal189000

Last rows

NeighborhoodOverallQualOverallCondYearBuiltRoofStyleExterior1stExterior2ndMasVnrTypeMasVnrAreaExterQualFoundationBsmtQualBsmtExposureBsmtFinType1BsmtFinSF1BsmtFinType2BsmtFinSF2BsmtUnfSFTotalBsmtSFHeatingHeatingQC1stFlrSF2ndFlrSFGrLivAreaBsmtFullBathFullBathHalfBathBedroomAbvGrKitchenQualTotRmsAbvGrdFireplacesGarageTypeGarageYrBltGarageFinishGarageAreaWoodDeckSFOpenPorchSFSaleConditionSalePrice
2920MeadowV451970GableCemntBdCmentBdNone0.0TACBlockTANoRec252.0Unf0.0294.0546.0GasATA54654610920.0113TA60CarPort1970.0Unf286.0024Abnorml71000
2921Mitchel651976GablePlywoodPlywoodNone0.0TACBlockTAGdRec936.0LwQ396.0396.01728.0GasATA1728017280.0204TA80Attchd1976.0Unf574.0400Normal150900
2922Mitchel551976GablePlywoodPlywoodNone0.0TACBlockTANoALQ1606.0Unf0.0122.01728.0GasATA1728017282.0204TA80Detchd1976.0Unf560.000Family188000
2923Mitchel551977GableBrkFaceBrkFaceNone0.0TACBlockTANoALQ936.0Unf0.0190.01126.0GasAFa1126011261.0203TA51Attchd1977.0RFn484.029541Normal160000
2924Mitchel571960GableVinylSdVinylSdNone0.0TACBlockTANoALQ1224.0Unf0.00.01224.0GasAEx1224012241.0104TA71Detchd1960.0Unf576.04740Abnorml131000
2925Mitchel661984GableHdBoardHdBoardNone0.0TACBlockTAAvGLQ819.0Unf0.0184.01003.0GasATA1003010031.0103TA60Detchd1984.0Unf588.01200Normal142500
2926Mitchel551983GableHdBoardHdBoardNone0.0TACBlockGdAvBLQ301.0ALQ324.0239.0864.0GasATA90209021.0102TA50Attchd1983.0Unf484.01640Normal131000
2927Mitchel551992GableHdBoardWd ShngNone0.0TAPConcGdAvGLQ337.0Unf0.0575.0912.0GasATA97009700.0103TA60Attchd1979.0Unf0.08032Normal132000
2928Mitchel551974GableHdBoardHdBoardNone0.0TACBlockGdAvALQ1071.0LwQ123.0195.01389.0GasAGd1389013891.0102TA61Attchd1975.0RFn418.024038Normal170000
2929Mitchel751993GableHdBoardHdBoardBrkFace94.0TAPConcGdAvLwQ758.0Unf0.0238.0996.0GasAEx996100420000.0213TA91Attchd1993.0Fin650.019048Normal188000

Duplicate rows

Most frequent

NeighborhoodOverallQualOverallCondYearBuiltRoofStyleExterior1stExterior2ndMasVnrTypeMasVnrAreaExterQualFoundationBsmtQualBsmtExposureBsmtFinType1BsmtFinSF1BsmtFinType2BsmtFinSF2BsmtUnfSFTotalBsmtSFHeatingHeatingQC1stFlrSF2ndFlrSFGrLivAreaBsmtFullBathFullBathHalfBathBedroomAbvGrKitchenQualTotRmsAbvGrdFireplacesGarageTypeGarageYrBltGarageFinishGarageAreaWoodDeckSFOpenPorchSFSaleConditionSalePricecount
2SawyerW752000HipVinylSdVinylSdBrkFace23.0TAPConcExNoGLQ820.0Unf0.0348.01168.0GasAEx1168161927872.0426TA82BuiltIn2000.0Fin820.03120Normal2695003
0Edwards551987GablePlywoodPlywoodNone0.0TACBlockGdGdGLQ1200.0Unf0.00.01200.0GasATA1200012003.0303TA50Attchd1979.0Unf0.01200Alloca1790002
1NridgHt852006GableVinylSdVinylSdStone108.0GdPConcGdMnGLQ24.0Unf0.01530.01554.0GasAEx1554015540.0202Gd61Attchd2006.0RFn627.015673Normal2095002
3Somerst752007GableVinylSdVinylSdNone0.0GdPConcGdNoUnf0.0Unf0.0689.0689.0GasAEx70368913920.0202Gd50Detchd2007.0Unf540.00102Abnorml1460002